As is known, modern electronic vehicle engine control units implement a plurality of algorithms that, when the engine is running, estimate engine quantities based on which the electronic control unit controls the engine operation.
These algorithms generally operate by using input quantities, of the motor type for example, generally measured by sensors when the engine is running, and experimentally determined calibration maps, which describe the trend of the quantity estimated by the algorithm, as a function of the quantities on which it depends.
As a rule, before being stored in the electronic control unit, the algorithms are calibrated using the aforementioned maps.
For example, the algorithm for estimating the instantaneous torque supplied by the engine, normally uses the number of engine revs RPM and/or the position of the accelerator pedal as input quantities, both of these detected by suitable sensors, and one or more algorithm calibration maps that describe the trend of supplied torque as a function of the number of engine revs RPM and/or position of the accelerator pedal, with the values of which the algorithm calculates each value of estimated torque.
In particular, the calibration maps of the algorithm are defined by experimentally measuring, on an engine test bench or a rolling road for vehicles, the motor quantities that will be estimated by the algorithm, as a function of the variables on which these depend, for example the torque supplied by the engine can be measured as a function of the number of revs RPM.
Carrying out the measurements of the quantities specified in the calibration maps and the calibration of the control unit's algorithms are operations that require rather long times, are particularly onerous and weigh significantly on the development costs of vehicle control units. Furthermore, the need to implement increasingly complex algorithms in the control units to carry out calculations on the basis of quantities supplied by a plurality of maps makes the process of calibrating the algorithms, consisting in the definition of map values, even longer and more complicated.
In order to simplify the calibration procedure of the algorithms, the following, for example, are known of: use of approximation formulas that describe the physics of the phenomenon to be represented, use of specific programming languages needed to be able to use algebraic formulas via which optimal parameter values can be calculated, or breaking down the algorithms into simpler algorithms and calibrating each one of them using specifically acquired data. For example, if the torque supplied by the engine depends on the product of the output of two calibration maps, usually the representative physical quantities of each of the two maps are measured and then each map is calibrated independently.
However, these solutions have several drawbacks, including:                the need to carry out specific measurements for calibration of the algorithm,        the need to carry out the measurements in special environmental and/or engine conditions,        the use of additional sensors for the acquisition of all the input and output quantities of the maps,        the propagation of measurement errors in the calibration procedure,        the poor precision of the simplified formulas utilized for describing the physical phenomenon,        the imprecision and difficulty of specific programming languages used for implementing the algorithm.        
Thus, the need is felt to reduce the number of experimental measurements necessary for obtaining the maps to the bare minimum and to implement an optimization method for the calibration maps of the algorithms that at least partially overcome the drawbacks of the known methods.